diff --git a/epimargin/policy.py b/epimargin/policy.py
index fc84dba..eeec659 100644
--- a/epimargin/policy.py
+++ b/epimargin/policy.py
@@ -246,7 +246,7 @@ def __init__(self, daily_doses: int, effectiveness: float, bin_populations: np.a
     def name(self) -> str:
         return f"{self.label}prioritized"
 
-    def distribute_doses(self, model: SIR, num_sims: int = 10_000) -> Tuple[np.ndarray]:
+    def distribute_doses(self, model: SIR, num_sims: int = 10_000) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
         if self.exhausted(model):
             return (None, None, None)
             # return (np.zeros(self.age_ratios.shape), np.zeros(self.age_ratios.shape), np.zeros(self.age_ratios.shape))
@@ -254,7 +254,7 @@ def distribute_doses(self, model: SIR, num_sims: int = 10_000) -> Tuple[np.ndarr
         model.S[-1] -= dV
         model.parallel_forward_epi_step(num_sims = num_sims)
 
-        dVx = np.zeros(self.bin_populations.shape)
+        dVx : np.ndarray = np.zeros(self.bin_populations.shape)
         bin_idx, age_bin = next(((i, age_bin) for (i, age_bin) in enumerate(self.prioritization) if self.bin_populations[age_bin] > 0), (None, None))
         if age_bin is not None:
             if self.bin_populations[age_bin] > self.daily_doses:
@@ -269,6 +269,7 @@ def distribute_doses(self, model: SIR, num_sims: int = 10_000) -> Tuple[np.ndarr
                     self.bin_populations[self.prioritization[bin_idx + 1]] -= leftover
         else:
             print("vaccination exhausted", self.bin_populations, self.prioritization)
+
         return (
             dVx,
             dVx * self.effectiveness,